Cover Image

mlr3mbo: Bayesian Optimization in R

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Becker, Marc, Schneider, Lennart, Binder, Martin, Kotthoff, Lars, Bischl, Bernd
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

mlr3torch: A Deep Learning Framework in R based on mlr3 and torch
by: Fischer, Sebastian, et al.
Published: (2026)

mlr3summary: Concise and interpretable summaries for machine learning models
by: Dandl, Susanne, et al.
Published: (2024)

mlr-org/mlr3tuning: mlr3tuning 1.4.0
by: Marc Becker, et al.
Published: (2025)

Overtuning in Hyperparameter Optimization
by: Schneider, Lennart, et al.
Published: (2025)

Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization
by: Nagler, Thomas, et al.
Published: (2024)

CASHomon Sets: Efficient Rashomon Sets Across Multiple Model Classes and their Hyperparameters
by: Ewald, Fiona Katharina, et al.
Published: (2026)

Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview
by: Karl, Florian, et al.
Published: (2022)

Trust Me, I Know the Way: Predictive Uncertainty in the Presence of Shortcut Learning
by: Wimmer, Lisa, et al.
Published: (2025)

fmeffects: An R Package for Forward Marginal Effects
by: Löwe, Holger, et al.
Published: (2023)

Smoothing the Edges: Smooth Optimization for Sparse Regularization using Hadamard Overparametrization
by: Kolb, Chris, et al.
Published: (2023)

xplainfi: Feature Importance and Statistical Inference for Machine Learning in R
by: Burk, Lukas, et al.
Published: (2026)

Hyperband-based Bayesian Optimization for Black-box Prompt Selection
by: Schneider, Lennart, et al.
Published: (2024)

Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
by: Sommer, Emanuel, et al.
Published: (2024)

Differentiable Sparsity via $D$-Gating: Simple and Versatile Structured Penalization
by: Kolb, Chris, et al.
Published: (2025)

Calibrating LLMs with Information-Theoretic Evidential Deep Learning
by: Li, Yawei, et al.
Published: (2025)

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries
by: Kolb, Chris, et al.
Published: (2025)

On the Robustness of Global Feature Effect Explanations
by: Baniecki, Hubert, et al.
Published: (2024)

Efficient and Accurate Explanation Estimation with Distribution Compression
by: Baniecki, Hubert, et al.
Published: (2024)

What Is Fairness? On the Role of Protected Attributes and Fictitious Worlds
by: Bothmann, Ludwig, et al.
Published: (2022)

Structured Credal Learning
by: Venkatesh, Varun, et al.
Published: (2026)

Analyzing Error Sources in Global Feature Effect Estimation
by: Heiß, Timo, et al.
Published: (2026)

Post-hoc Orthogonalization for Mitigation of Protected Feature Bias in CXR Embeddings
by: Weber, Tobias, et al.
Published: (2023)

Deep Learning for Survival Analysis: A Review
by: Wiegrebe, Simon, et al.
Published: (2023)

Uncertainty-aware Pseudo-label Selection for Positive-Unlabeled Learning
by: Dorigatti, Emilio, et al.
Published: (2022)

Exploring Exploration in Bayesian Optimization
by: Papenmeier, Leonard, et al.
Published: (2025)

Optimal Transport Group Counterfactual Explanations
by: Valero-Leal, Enrique, et al.
Published: (2026)

Decomposing Global Feature Effects Based on Feature Interactions
by: Herbinger, Julia, et al.
Published: (2023)

Revisiting Unbiased Implicit Variational Inference
by: Pielok, Tobias, et al.
Published: (2025)

Semi-Implicit Variational Inference via Kernelized Path Gradient Descent
by: Pielok, Tobias, et al.
Published: (2025)

Mind the Gap: Measuring Generalization Performance Across Multiple Objectives
by: Feurer, Matthias, et al.
Published: (2022)

On Training Survival Models with Scoring Rules
by: Kopper, Philipp, et al.
Published: (2024)

Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis
by: Scholbeck, Christian A., et al.
Published: (2023)

A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data
by: Burk, Lukas, et al.
Published: (2024)

CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forests
by: Dandl, Susanne, et al.
Published: (2024)

Constructing Confidence Intervals for 'the' Generalization Error -- a Comprehensive Benchmark Study
by: Schulz-Kümpel, Hannah, et al.
Published: (2024)

Privilege Scores
by: Bothmann, Ludwig, et al.
Published: (2025)

A Dual-Perspective Approach to Evaluating Feature Attribution Methods
by: Li, Yawei, et al.
Published: (2023)

A Unified Framework for Entropy Search and Expected Improvement in Bayesian Optimization
by: Cheng, Nuojin, et al.
Published: (2025)

FinerCut: Finer-grained Interpretable Layer Pruning for Large Language Models
by: Zhang, Yang, et al.
Published: (2024)

AttributionLab: Faithfulness of Feature Attribution Under Controllable Environments
by: Zhang, Yang, et al.
Published: (2023)